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Distributed model for customer churn prediction using convolutional neural network

Distributed model for customer churn prediction using convolutional neural network The purpose of the proposed model is to assist the e-business to predict the churned users using machine learning. This paper aims to monitor the customer behavior and to perform decision-making accordingly.Design/methodology/approachThe proposed model uses the 2-D convolutional neural network (CNN; a technique of deep learning). The proposed model is a layered architecture that comprises two different phases that are data load and preprocessing layer and 2-D CNN layer. In addition, the Apache Spark parallel and distributed framework is used to process the data in a parallel environment. Training data is captured from Kaggle by using Telco Customer Churn.FindingsThe proposed model is accurate and has an accuracy score of 0.963 out of 1. In addition, the training and validation loss is extremely less, which is 0.004. The confusion matric results show the true-positive values are 95% and the true-negative values are 94%. However, the false-negative is only 5% and the false-positive is only 6%, which is effective.Originality/valueThis paper highlights an inclusive description of preprocessing required for the CNN model. The data set is addressed more carefully for the successful customer churn prediction. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Modelling in Management Emerald Publishing

Distributed model for customer churn prediction using convolutional neural network

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1746-5664
eISSN
1746-5664
DOI
10.1108/jm2-01-2021-0032
Publisher site
See Article on Publisher Site

Abstract

The purpose of the proposed model is to assist the e-business to predict the churned users using machine learning. This paper aims to monitor the customer behavior and to perform decision-making accordingly.Design/methodology/approachThe proposed model uses the 2-D convolutional neural network (CNN; a technique of deep learning). The proposed model is a layered architecture that comprises two different phases that are data load and preprocessing layer and 2-D CNN layer. In addition, the Apache Spark parallel and distributed framework is used to process the data in a parallel environment. Training data is captured from Kaggle by using Telco Customer Churn.FindingsThe proposed model is accurate and has an accuracy score of 0.963 out of 1. In addition, the training and validation loss is extremely less, which is 0.004. The confusion matric results show the true-positive values are 95% and the true-negative values are 94%. However, the false-negative is only 5% and the false-positive is only 6%, which is effective.Originality/valueThis paper highlights an inclusive description of preprocessing required for the CNN model. The data set is addressed more carefully for the successful customer churn prediction.

Journal

Journal of Modelling in Management – Emerald Publishing

Published: Aug 22, 2022

Keywords: Artificial intelligence; Data analysis; Computing; Churn prediction; Big data; Machine learning; Social network analysis

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